# Can I use numpy.polyfit(x, y, deg) for multiple linear regression

Is there any way I can fit two independent variables and one dependent variable in numpy.polyfit()?

I have a panda data frame that I loaded from a csv file. I wish to include two columns as independent variables to run multiple linear regression using NumPy.

Currently my simple linear regression looks like this:

`model_combined = np.polyfit(data.Exercise, y, 1)`

I wish to include `data.Age` in x as well.

Assuming your equation is `a * exercise + b * age + intercept = y`, you can fit a multiple linear regression with numpy or scikit-learn as follows:

```from sklearn import linear_model
import numpy as np
np.random.seed(42)

X = np.random.randint(low=1, high=10, size=20).reshape(10, 2)
X = np.c_[X, np.ones(X.shape)]  # add intercept
y = np.random.randint(low=1, high=10, size=10)

# Option 1
a, b, intercept = np.linalg.pinv((X.T).dot(X)).dot(X.T.dot(y))
print(a, b, intercept)

# Option 2
a, b, intercept = np.linalg.lstsq(X,y, rcond=None)
print(a, b, intercept)

# Option 3
clf = linear_model.LinearRegression(fit_intercept=False)
clf.fit(X, y)
print(clf.coef_)
```
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